Cambridge, MA — November 3, 2005 — In the sci-fi movie The Matrix, a cable running from a computer into Neo's brain writes in visual perceptions, and the brain reads out instructions, such as when to whirl his long trench coat. In reality, scientists cannot interact directly with the brain because they do not understand enough about how it codes and decodes information. Now, neuroscientists in the McGovern Institute at MIT have been able to read out a part of the visual system's code involved in recognizing visual objects. The study, a collaboration between James DiCarlo's and Tomaso Poggio's labs, appears in the November 4 issue of Science.

"We want to know how the brain works to create intelligence," Poggio explains. "Our ability to recognize objects in the visual world is among the most complex problems the brain must solve. Computationally, it is much harder than reasoning." Yet we take it for granted because it appears to happen automatically and almost unconsciously.

"This work enhances our understanding of how the brain encodes visual information in a useful format for brain regions involved in action, planning, and memory," says DiCarlo. Practically speaking, computer algorithms used in artificial vision systems might benefit from mimicking these newly uncovered codes. Eventually figuring out the writing-in process could help the blind see.

In a fraction of a second, visual input about an object runs from the retina through increasingly higher levels of the visual stream, continuously reformatting the information until it reaches the highest purely visual level, the inferotemporal cortex (IT). The IT cortex identifies and categorizes the object, and sends that information to other brain regions.

To explore how the IT cortex formats that output, the researchers trained monkeys to recognize different objects grouped into categories, such as faces, toys, and vehicles. The images appeared in different sizes and positions in the visual field. Recording the activity of hundreds of IT neurons produced a large database of IT neural patterns in response to each object under many different conditions.

Then, the researchers used a computer algorithm, called a classifier, to decipher the code. The classifier was used to associate each object - say, a monkey's face -- with a particular pattern of neural signals, effectively decoding neural activity. Remarkably, the classifier found that even just a split second's worth of the neural signal contained specific enough information to identity and categorize the object, even at positions and sizes the classifier not previously "seen."

It was quite surprising that so few IT neurons (several hundred out of millions) for such a short period time contained so much precise information. "If we could record a larger population of neurons simultaneously," Poggio says, "we might find even more robust codes hidden in the neural patterns and extract even fuller information."

Neurons in the last purely visual brain region, the inferotemporal (IT) cortex, respond selectively to different images. As pictures were randomly presented to the monkey during specific intervals (top), neurons at different sites in IT produce distinct patterns of activity to each picture (bottom). For example, neurons at site 1 favor the toy and the yam, while neurons at site 3 prefer the monkey face and the cat. Combining just ~100 recording sites is enough to provide highly accurate information about the picture and category that both a simple classifier and downstream neurons in more cognitive brain regions can decode.

About the McGovern Institute at MIT

The McGovern Institute at MIT is a research and teaching institute committed to advancing human understanding and communications. Led by a team of world-renowned, multi-disciplinary scientists, The McGovern Institute was established in February 2000 by Lore Harp McGovern and Patrick McGovern to meet one of the great challenges of modern science - the development of a deep understanding of thought and emotion in terms of their realization in the human brain. Additional information is available at: http://web.mit.edu/mcgovern

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